Standard contrastive learning approaches implicitly assume that the positive and the negative belong to different categories in downstream tasks [1]}.
[2]} states that data augmentation strategies should preserve the intrinsic structure and attributes of the graph.
Our design adheres to the above constraints.
Considering the representation of original graph \(\mathcal {G}\)  as the anchor, we need to construct the positive and the negative under two other graph views (i.e., \(\mathcal {G}^\rho \)  and \(\mathcal {G}^\eta \) ) for contrastive learning.
This process can be described as follows.
